Rico: A Mobile App Dataset for Building Data-Driven Design Applications

被引：257

作者：

Deka, Biplab ^{[1
]}

Huang, Zifeng ^{[1
]}

Franzen, Chad ^{[1
]}

Hibschman, Joshua ^{[2
]}

Afergan, Daniel ^{[3
]}

Li, Yang ^{[3
]}

Nichols, Jeffrey ^{[3
]}

Kumar, Ranjitha ^{[1
]}

机构：

[1] Univ Illinois, Champaign, IL 61820 USA

[2] Northwestern Univ, Evanston, IL 60208 USA

[3] Google Inc, Mountain View, CA USA

来源：

UIST'17: PROCEEDINGS OF THE 30TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY | 2017年

关键词：

Mobile app design; design mining; design search; app datasets;

D O I：

10.1145/3126594.3126651

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Data-driven models help mobile app designers understand best practices and trends, and can be used to make predictions about design performance and support the creation of adaptive UIs. This paper presents Rico, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, Ut code generation, user interaction modeling, and user perception prediction. To create Rico, we built a system that combines crowdsourcing and automation to scalably mine design and interaction data from Android apps at runtime. The Rico dataset contains design data from more than 9.7k Android apps spanning 27 categories. It exposes visual, textual, structural, and interactive design properties of more than 72k unique UI screens. To demonstrate the kinds of applications that Rico enables, we present results from training an autoencoder for UI layout similarity, which supports query-by-example search over UIs,

引用

页码：845 / 854

页数：10

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